Erik Paulson 0001

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from EDBT 2026 / reviewers in the wild / expert

dblp:22/3083-1 · DBLP profile ↗

← Back

6ranked-venue papers

0as first author

0since 2021 · last 2019

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 5Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval 5 papers		Data integration and cleaning · 66% Query processing and optimization · 26% Database system architecture and tuning · 9%
Computer architecture, parallel and distributed computing, and storage systems 5 papers		Cloud and datacenter computing · 66% Performance modeling and evaluation · 21% Distributed systems · 10%
Software engineering, system software, and programming languages 1 paper		Services computing and microservices · 100%

Topics — the 10 heaviest of 13, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Data integration and cleaning entity matching	0.7	2	2019	Entity Matching Meets Data Science: A Progress Report from the Magellan Project · SIGMOD Conference 2019 CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching · Proc. VLDB Endow. 2018
Query processing and optimization › query optimization › transformation-based optimization query pushdown	0.1	1	2011	Efficient processing of data warehousing queries in a split execution environment · SIGMOD Conference 2011
Query processing and optimization SQL query processing	0.1	1	2011	Efficient processing of data warehousing queries in a split execution environment · SIGMOD Conference 2011
Database system architecture and tuning parallel database system	0.1	1	2009	A comparison of approaches to large-scale data analysis · SIGMOD Conference 2009
Performance modeling and evaluation benchmarking	0.1	1	2009	A comparison of approaches to large-scale data analysis · SIGMOD Conference 2009
Cloud and datacenter computing › cluster resource management and scheduling cluster resource management	0.1	1	2008	Clustera: an integrated computation and data management system · Proc. VLDB Endow. 2008
Cloud and datacenter computing job scheduling	0.1	1	2008	Clustera: an integrated computation and data management system · Proc. VLDB Endow. 2008
Distributed systems grid computing	0.0	1	2004	The Grid2003 Production Grid: Principles and Practice · HPDC 2004
Cloud and datacenter computing › cluster computing framework mapreduce framework	0.0	1	2011	Efficient processing of data warehousing queries in a split execution environment · SIGMOD Conference 2011
High-performance computing distributed computing infrastructure	0.0	1	2004	The Grid2003 Production Grid: Principles and Practice · HPDC 2004

Methods — techniques the papers use, named apart from their topics

interactive labeling · 0.7crowdsourcing · 0.7query pushdown · 0.2mapreduce · 0.2

Year	Publication	Venue	Position
2019	Entity Matching Meets Data Science: A Progress Report from the Magellan Project abstract Entity matching (EM) finds data instances that refer to the same real-world entity. In 2015, we started the Magellan project at UW-Madison, joint with industrial partners, to build EM systems. Most current EM systems are stand-alone monoliths. In contrast, Magellan borrows ideas from the field of data science (DS), to build a new kind of EM systems, which is an ecosystem of interoperable tools. \em This paper provides a progress report on the past 3.5 years of Magellan, focusing on the system aspects and on how ideas from the field of data science have been adapted to the EM context. We argue why EM can be viewed as a special class of DS problems, and thus can benefit from system building ideas in DS. We discuss how these ideas have been adapted to build \pymatcher\ and \cloudmatcher, EM tools for power users and lay users. These tools have been successfully used in 21 EM tasks at 12 companies and domain science groups, and have been pushed into production for many customers. We report on the lessons learned, and outline a new envisioned Magellan ecosystem, which consists of not just on-premise Python tools, but also interoperable microservices deployed, executed, and scaled out on the cloud, using tools such as Dockers and Kubernetes. Yash Govind, Pradap Konda, Paul Suganthan G. C., Philip Martinkus, Palaniappan Nagarajan, Aravind Soundararajan, Sidharth Mudgal, Jeffrey R. Ballard, Haojun Zhang, Adel Ardalan, Sanjib Das, Derek Paulsen, Amanpreet Singh Saini, Erik Paulson 0001, Youngchoon Park, Marshall Carter, Mingju Sun, Glenn Fung, AnHai Doan	SIGMOD Conference	15
2018	CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching abstract As data science applications proliferate, more and more lay users must perform data integration (DI) tasks, which used to be done by sophisticated CS developers. Thus, it is increasingly critical that we develop hands-off DI services, which lay users can use to perform such tasks without asking for help from developers. We propose to demonstrate such a service. Specifically, we will demonstrate CloudMatcher, a hands-off cloud/crowd service for entity matching (EM). To use CloudMatcher to match two tables, a lay user only needs to upload them to the CloudMatcher's Web page then iteratively label a set of tuple pairs as match/no-match. Alternatively, the user can enlist a crowd of workers to label the pairs. In either case, the lay user can easily perform EM end-to-end without having to involve any developers. Cloud-Matcher has been used in several domain science projects at UW-Madison and at several organizations, and is scheduled to be deployed in a large company in Summer 2018. In the demonstration we will show how easy it is for lay users to perform EM (either via interactive labeling or crowdsourcing), how users can easily create and experiment with a range of EM workflows, and how CloudMatcher can scale to many concurrent users and large datasets. Yash Govind, Erik Paulson 0001, Palaniappan Nagarajan, Paul Suganthan G. C., AnHai Doan, Youngchoon Park, Glenn Fung, Devin Conathan, Marshall Carter, Mingju Sun	Proc. VLDB Endow.	2
2011	Efficient processing of data warehousing queries in a split execution environment abstract Hadapt is a start-up company currently commercializing the Yale University research project called HadoopDB. The company focuses on building a platform for Big Data analytics in the cloud by introducing a storage layer optimized for structured data and by providing a framework for executing SQL queries efficiently. This work considers processing data warehousing queries over very large datasets. Our goal is to maximize perfor mance while, at the same time, not giving up fault tolerance and scalability. We analyze the complexity of this problem in the split execution environment of HadoopDB. Here, incoming queries are examined; parts of the query are pushed down and executed inside the higher performing database layer; and the rest of the query is processed in a more generic MapReduce framework. Kamil Bajda-Pawlikowski, Daniel J. Abadi, Avi Silberschatz, Erik Paulson 0001	SIGMOD Conference	4
2009	A comparison of approaches to large-scale data analysis abstract There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures. Andrew Pavlo, Erik Paulson 0001, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden 0001, Michael Stonebraker	SIGMOD Conference	2
2008	Clustera: an integrated computation and data management system abstract This paper introduces Clustera, an integrated computation and data management system. In contrast to traditional cluster-management systems that target specific types of workloads, Clustera is designed for extensibility, enabling the system to be easily extended to handle a wide variety of job types ranging from computationally-intensive, long-running jobs with minimal I/O requirements to complex SQL queries over massive relational tables. Another unique feature of Clustera is the way in which the system architecture exploits modern software building blocks including application servers and relational database systems in order to realize important performance, scalability, portability and usability benefits. Finally, experimental evaluation suggests that Clustera has good scale-up properties for SQL processing, that Clustera delivers performance comparable to Hadoop for MapReduce processing and that Clustera can support higher job throughput rates than previously published results for the Condor and CondorJ2 batch computing systems. David J. DeWitt, Erik Paulson 0001, Eric Robinson, Jeffrey F. Naughton, Joshua Royalty, Srinath Shankar, Andrew Krioukov	Proc. VLDB Endow.	2
2004	The Grid2003 Production Grid: Principles and Practice Ian T. Foster, Jerry Gieraltowski, Scott Gose, Natalia Maltsev, Edward N. May, Alexis A. Rodriguez, Dinanath Sulakhe, A. Vaniachine, Jim Shank, Saul Youssef, David Adams, Richard Baker 0003, Wensheng Deng, Dantong Yu, Iosif Legrand, Conrad Steenberg, M. Anzar Afaq, Eileen Berman, James Annis, L. A. T. Bauerdick, Michael Ernst, Ian Fisk, Lisa Giacchetti, Gregory E. Graham, Anne Heavey, Joseph Kaiser, Nickolai Kuropatkin, Ruth Pordes, Vijay Sekhri, John Weigand, Yujun Wu, Keith Baker, Lawrence Sorrillo, John Huth, Matthew Allen, Leigh Grundhoefer, John Hicks, Fred Luehring, Steve Peck, Robert Quick, Stephen C. Simms, George Fekete, Jan vandenBerg, Kihyeon Cho, Kihwan Kwon, Dongchul Son, Hyoungwoo Park, Shane Canon, Keith R. Jackson, David E. Konerding, Jason Lee 0001, Doug Olson, Iwona Sakrejda, Brian Tierney, Mark Green 0001, Russ Miller, James Letts, Terrence Martin, David Bury, Catalin Dumitrescu, Daniel Engh, Robert W. Gardner, Marco Mambelli, Yuri Smirnov, Jens-S. Vöckler, Michael Wilde, Yong Zhao 0009, Paul Avery, Richard Cavanaugh, Bockjoo Kim, Craig Prescott, Jorge Rodríguez 0002, Andrew Zahn, Shawn McKee, Christopher T. Jordan, James E. Prewett, Timothy L. Thomas, Horst Severini, Ben Clifford, Ewa Deelman, Larry Flon, Carl Kesselman, Gaurang Mehta, Nosa Olomu, Karan Vahi, Kaushik De, Patrick McGuigan, Mark Sosebee, Dan Bradley, Peter Couvares, Alan DeSmet, Carey Kireyev, Erik Paulson 0001, Alain J. Roy, Scott Koranda, Brian Moe, Bobby Brown, Paul Sheldon	HPDC	97